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\begin{abstract}  Teste  \end{abstract}  \section{Introduction}  In the last decades, advances in remote sensing image acquisition systems have moved in lockstep with the need for applications that make use of such sort of data. Land-use/cover recognition~\cite{Pisani_2014,Bagan_2010,Capucim_2015,Banerjee_2015}, target recognition~\cite{Dong_2015,Martorella_2011,Du_2011}, image classification~\cite{Li_2015,Voisin_2014} and band selection in hyper-spectral images~\cite{He_Yang_2011,Yuan_Yuan_2015} are among the most pursued applications, just to name a few. The large amount of high-resolution content available by satellites also highlights the bottleneck that takes place when labeling data. Such process is skilled-dependent, and it might be very prone to errors when dealing with manual annotation. Such shortcomings have fostered even more the research on semi-supervised and unsupervised techniques, which may work well in some remote sensing-oriented applications.